1. Field of the Invention
The invention relates to the field of inertial sensors and instrumentation, and in particular to the processing of redundant inertial sensor data to improve accuracy, using a Monte Carlo estimation-based inference system applicable to estimating linear, non-linear, Gaussian and non-Gaussian systems.
2. Background
The inertial measurement unit (IMU) is a critical component for a wide range of applications ranging from aerospace and missile guidance systems, to robotics and navigation systems, to interactive games and simulations. IMU systems incorporate angular rate sensors as a system-critical component that detects changes in the orientation of the system. Gyroscopic sensor system performance is generally dominated by two critical performance characteristics: bias drift and noise components. These include inaccuracies induced by sources such as bias drift, rate random walk, angular random walk, process noise, measurement noise, thermal noise, and even sensor failure.
Recently, gyroscopic rate sensors enabled by micro-electro-mechanical systems (MEMS) technologies have produced revolutionary reductions in size, cost, and power consumption over traditional electro-optical and electro-mechanical systems. Additionally, since MEMS-based systems are packaged as and used in production as any other semiconductor device, final integrated systems can be more rugged and more easily mass produced. Despite these tremendous advantages however, current state-of-the-art MEMS rate sensors cannot compete with traditional non-MEMS gyroscopic systems with respect to bias drift and noise rejection.
Bias drift, the tendency to lose position over time, is a pervasive problem within MEMS products that must be avoided or controlled at the component level and/or at the control/algorithmic level. MEMS components are subject to drift due to surface charging in some cases, and most commonly due to stress on and within the MEMS units. As the units are put under temperature pressure, physical stress, and other environmental factors, stresses between the materials in the MEMS units create signals that look similar to an electrical velocity or acceleration signal, thus causing integrative inertial calculations to drift. Traditionally, developers have opted for larger units, additional packaging layers, tight temperature control, and filtering to reduce bias drift.
Noise, resulting from a number of physical phenomena, generally exhibits random properties that are difficult to compensate for via packaging or filtering approaches. These random noise effects, classified as angular random walk, rate random walk, and angular white noise, are, by nature, unpredictable and therefore difficult to counteract. With a single sensor configuration, differentiating noise from inertial rate becomes a daunting task, especially in MEMS systems which, due to their micro-electro-mechanical structure, have significant process noise magnitudes. Understanding and characterizing these process noise components can be useful in calculating sensor reading confidence intervals, but can do little to eliminate or counteract the noise since in single sensor systems there is no additional frame of reference by which noise can be cross-correlated.
Thus, gyroscopic rate sensor systems can be mechanical, electro-optical, or MEMS. While traditional electro-mechanical and electro-optical systems can be made to exhibit environmental noise rejection and low bias drift characteristics, they are, by nature, larger and thus more difficult to adapt to applications where small size and/or low-power requirements are important concerns. Recent efforts have focused upon using MEMS sensors that, while resulting in smaller sizes and lower power requirements, have not resulted in desired environmental noise rejection or low-drift characteristics.